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Electronics
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Deep/Machine Learning in Visual Recognition and Anomaly Detection
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Deep/Machine Learning in Visual Recognition and Anomaly Detection

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 15 July 2025 | Viewed by 3481

Special Issue Editors

Dr. Yang Liu

E-Mail Website
Guest Editor
School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: computer vision; pattern recognition; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Jin Li

E-Mail Website
Guest Editor
School of Computer Science, Shaanxi Normal University, Xi’an 710062, China
Interests: computer vision; artificial intelligence

Special Issue Information

Dear Colleagues，

The success of machine learning and deep neural networks have facilitated advances in understanding the high-level semantics of visual content. Conventional learning-based visual semantic recognition approaches rely heavily on large-scale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories. The emergence and rapid progress of few-/zero-shot learning make it possible to learn unseen categories from a few labeled or even zero-labeled samples, which advances the extension to practical applications. This Special Issue aims to demonstrate (1) how machine learning algorithms have contributed, and are contributing, to new theories, models, and datasets related to the topic of few-/zero-shot learning; (2) how few-/zero-shot learning can facilitate other tasks such as visual recognition and anomaly detection. The editors hope to collate a group of research results to report the recent developments in the related research topics. In addition, researchers can exchange their innovative ideas on the topic of few-/zero-shot learning in visual recognition and anomaly detection by submitting manuscripts for this Special Issue.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

(1) Theoretical advances and algorithm developments in few-/zero-shot learning;

(2) Useful applications of few-/zero-shot learning in visual recognition and anomaly detection;

(3) New datasets and benchmarks for few-/zero-shot learning in visual recognition and anomaly detection.

We look forward to receiving your contributions.

Dr. Yang Liu
Dr. Jin Li
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • few-/zero-shot learning
  • visual recognition
  • anomaly detection

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Published Papers (3 papers)

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Research

14 pages, 4005 KiB  
Article
A Directional Enhanced Adaptive Detection Framework for Small Targets
by Chao Li, Yifan Chang, Shimeng Yang, Kaiju Li and Guangqiang Yin
Electronics 2024, 13(22), 4535; https://doi.org/10.3390/electronics13224535 - 19 Nov 2024
Viewed by 553
Abstract
Due to the challenges posed by limited size and features, positional and noise issues, and dataset imbalance and simplicity, small object detection is one of the most challenging tasks in the field of object detection. Consequently, an increasing number of researchers are focusing [...] Read more.
Due to the challenges posed by limited size and features, positional and noise issues, and dataset imbalance and simplicity, small object detection is one of the most challenging tasks in the field of object detection. Consequently, an increasing number of researchers are focusing on this area. In this paper, we propose a Directional Enhanced Adaptive (DEA) detection framework for small targets. This framework effectively combines the detection accuracy advantages of two-stage methods with the detection speed advantages of one-stage methods. Additionally, we introduce a Multi-Scale Object Adaptive Slicing (MASA) module and an improved IoU-based aggregation module that integrate with this framework to enhance detection performance. For better comparison, we use the F1 score as one of the evaluation metrics. The experimental results demonstrate that our DEA framework improves the performance of various backbone detection networks and achieves better comprehensive detection performance than other proposed methods, even though our network has not been trained on the test dataset while others have. Full article
(This article belongs to the Special Issue Deep/Machine Learning in Visual Recognition and Anomaly Detection)
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16 pages, 531 KiB  
Article
A Robust Generalized Zero-Shot Learning Method with Attribute Prototype and Discriminative Attention Mechanism
by Xiaodong Liu, Weixing Luo, Jiale Du, Xinshuo Wang, Yuhao Dang and Yang Liu
Electronics 2024, 13(18), 3751; https://doi.org/10.3390/electronics13183751 - 21 Sep 2024
Viewed by 1253
Abstract
In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the [...] Read more.
In the field of Generalized Zero-Shot Learning (GZSL), the challenge lies in learning attribute-based information from seen classes and effectively conveying this knowledge to recognize both seen and unseen categories during the training process. This paper proposes an innovative approach to enhance the generalization ability and efficiency of GZSL models by integrating a Convolutional Block Attention Module (CBAM). The CBAM blends channel-wise and spatial-wise information to emphasize key features, thereby improving the model’s discriminative and localization capabilities. Additionally, the method employs a ResNet101 backbone for systematic image feature extraction, enhanced contrastive learning, and a similarity map generator with attribute prototypes. This comprehensive framework aims to achieve robust visual–semantic embedding for classification tasks. The proposed method demonstrates significant improvements in performance metrics in benchmark datasets, showcasing its potential in advancing GZSL applications. Full article
(This article belongs to the Special Issue Deep/Machine Learning in Visual Recognition and Anomaly Detection)
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18 pages, 1072 KiB  
Article
Leveraging Self-Distillation and Disentanglement Network to Enhance Visual–Semantic Feature Consistency in Generalized Zero-Shot Learning
by Xiaoming Liu, Chen Wang, Guan Yang, Chunhua Wang, Yang Long, Jie Liu and Zhiyuan Zhang
Electronics 2024, 13(10), 1977; https://doi.org/10.3390/electronics13101977 - 18 May 2024
Viewed by 1084
Abstract
Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based [...] Read more.
Generalized zero-shot learning (GZSL) aims to simultaneously recognize both seen classes and unseen classes by training only on seen class samples and auxiliary semantic descriptions. Recent state-of-the-art methods infer unseen classes based on semantic information or synthesize unseen classes using generative models based on semantic information, all of which rely on the correct alignment of visual–semantic features. However, they often overlook the inconsistency between original visual features and semantic attributes. Additionally, due to the existence of cross-modal dataset biases, the visual features extracted and synthesized by the model may also mismatch with some semantic features, which could hinder the model from properly aligning visual–semantic features. To address this issue, this paper proposes a GZSL framework that enhances the consistency of visual–semantic features using a self-distillation and disentanglement network (SDDN). The aim is to utilize the self-distillation and disentanglement network to obtain semantically consistent refined visual features and non-redundant semantic features to enhance the consistency of visual–semantic features. Firstly, SDDN utilizes self-distillation technology to refine the extracted and synthesized visual features of the model. Subsequently, the visual–semantic features are then disentangled and aligned using a disentanglement network to enhance the consistency of the visual–semantic features. Finally, the consistent visual–semantic features are fused to jointly train a GZSL classifier. Extensive experiments demonstrate that the proposed method achieves more competitive results on four challenging benchmark datasets (AWA2, CUB, FLO, and SUN). Full article
(This article belongs to the Special Issue Deep/Machine Learning in Visual Recognition and Anomaly Detection)
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